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The MIMIC-CXR dataset is (to date) the largest released chest x-ray dataset consisting of 473,064 chest x-rays and 206,574 radiology reports collected from 63,478 patients.
Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules
Junji Shiraishi, Shigehiko Katsuragawa, Junpei Ikezoe, Tsuneo Matsumoto, Takeshi Kobayashi, Ken-ichi Komatsu, Mitate Matsui, Hiroshi Fujita, Yoshie Kodera, and Kunio Doi · 2000
Earlier work this paper cites.
Computer-aided diagnosis in chest radiography
Bram Van Ginneken · 2001
Earlier work this paper cites.
Computer-aided diagnosis in chest radiography: a survey
Bram Van Ginneken, BM Ter Haar Romeny, and Max A Viergever · 2001
Earlier work this paper cites.
Pneumothorax in the icu: patient outcomes and prognostic factors
Kuan-Yu Chen, Jih-Shuin Jerng, Wei-Yu Liao, Liang-Wen Ding, Lu-Cheng Kuo, Jann-Yuan Wang, and Pan-Chyr Yang · 2002
Earlier work this paper cites.
Medical devices of the chest
Tim B Hunter, Mihra S Taljanovic, Pei H Tsau, William G Berger, and James R Standen · 2004
Earlier work this paper cites.
Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database
Bram Van Ginneken, Mikkel B Stegmann, and Marco Loog · 2006
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Interpretation of plain chest roentgenogram
Suhail Raoof, David Feigin, Arthur Sung, Sabiha Raoof, Lavanya Irugulpati, and Edward C Rosenow · 2012
Earlier work this paper cites.
Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Stefan Jaeger, Sema Candemir, Sameer Antani, Yì-Xiáng J Wáng, Pu-Xuan Lu, and George Thoma · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Preparing a collection of radiology examinations for distribution and retrieval
Dina Demner-Fushman, Marc D Kohli, Marc B Rosenman, Sonya E Shooshan, Laritza Rodriguez, Sameer Antani, George R Thoma, and Clement J McDonald · 2015
Cited alongside, same era.
Deep residual learning for image recognition. corr abs/1512.03385 (2015), 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Negbio: a high-performance tool for negation and uncertainty detection in radiology reports
Yifan Peng, Xiaosong Wang, Le Lu, Mohammadhadi Bagheri, Ronald Summers, and Zhiyong Lu · 2017
Later among the works it cites.
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, et al · 2017
Later among the works it cites.
Cyclical learning rates for training neural networks
Leslie N Smith · 2017
Later among the works it cites.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
Later among the works it cites.
Learning to diagnose from scratch by exploiting dependencies among labels
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Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Yu Gordienko, Peng Gang, Jiang Hui, Wei Zeng, Yu Kochura, O Alienin, O Rokovyi, and S Stirenko · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Abnormality detection and localization in chest x-rays using deep convolutional neural networks
Mohammad Tariqul Islam, Md Abdul Aowal, Ahmed Tahseen Minhaz, and Khalid Ashraf · 2017
Cited alongside, same era.
Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs
Pulkit Kumar, Monika Grewal, and Muktabh Mayank Srivastava · 2017
Cited alongside, same era.
Li Yao, Eric Poblenz, Dmitry Dagunts, Ben Covington, Devon Bernard, and Kevin Lyman · 2017
Later among the works it cites.
Mastering ap and lateral positioning for chest x-ray
Naveed Ahmad · 2018
Closest in time.
Comparison of deep learning approaches for multi-label chest x-ray classification
Ivo M Baltruschat, Hannes Nickisch, Michael Grass, Tobias Knopp, and Axel Saalbach · 2018
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Positioning
Spencer B. Gay, Juan Olazagasti, Jack W. Higginbotham, Atul Gupta, Alex Wurm, and Jonathan Nguyen · 2018
Closest in time.
Qingji Guan, Yaping Huang, Zhun Zhong, Zhedong Zheng, Liang Zheng, and Yi Yang · 2018
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